Abstract
After suffering a myocardial infarction, patient’s tissue shows a complex substrate remodeling that combines dead and viable tissue in the scar region. Within such regions, slow conduction channels (SCC) might be present, being formed by viable tissue with altered electrical properties that can change the normal ventricle activation sequence, and sustain a ventricular tachycardia (VT) [8]. Computational models can help to stratify patients at risk, but they usually require large computational resources. In this study, we present a fast pipeline based on fully automatic modeling and simulation of patient’s electrophysiology to assess the potential arrhythmogeneity of SCCs based on hundreds of simulated scenarios per patient. We apply our pipeline to four patients that have suffered a myocardial infarction, reproducing successfully predicting patient arrhythmogeneity in all cases with low computational times compatible with clinical routine (less than 4 h).
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Serra, D. et al. (2022). Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity of Ventricular Slow Conduction Channels. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers. STACOM 2022. Lecture Notes in Computer Science, vol 13593. Springer, Cham. https://doi.org/10.1007/978-3-031-23443-9_6
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DOI: https://doi.org/10.1007/978-3-031-23443-9_6
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